There are two main kinds of architecture followed by the recent LP models.
The first uses an encoder, e.g., GCN [1]}, to obtain the node-level embeddings and uses a decoder, e.g., Dot Product, to get the edge scores between the paired nodes [2]}.
The second crops a subgraph for every edge and computes the edge score from the subgraph directly [3]}.
The inference speed of the second is much lower than the first, so we focus on the first kind of models to achieve fast inference on large graphs.
In the last years, extensive research focuses on developing more expressive LP encoders [2]}, [5]}.
However, much less work pays attention to the essential impacts of the choice of decoders on LP's performance.
In this work, we theoretically and empirically analyze two popular LP decoders: Dot Product and HadamardMLP (a MLP following the Hadamard Product), and find that the latter is generally more effective than the former.
